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_base_ = [ |
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'../_base_/datasets/coco_instance.py', |
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'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' |
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] |
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|
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model = dict( |
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type='HybridTaskCascade', |
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backbone=dict( |
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type='ResNet', |
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depth=50, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type='BN', requires_grad=True), |
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norm_eval=True, |
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style='pytorch', |
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), |
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neck=dict( |
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type='FPN', |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=256, |
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num_outs=5), |
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rpn_head=dict( |
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type='RPNHead', |
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in_channels=256, |
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feat_channels=256, |
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anchor_generator=dict( |
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type='AnchorGenerator', |
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scales=[8], |
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ratios=[0.5, 1.0, 2.0], |
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strides=[4, 8, 16, 32, 64]), |
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bbox_coder=dict( |
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type='DeltaXYWHBBoxCoder', |
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target_means=[.0, .0, .0, .0], |
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target_stds=[1.0, 1.0, 1.0, 1.0]), |
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loss_cls=dict( |
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), |
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loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), |
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roi_head=dict( |
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type='HybridTaskCascadeRoIHead', |
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interleaved=True, |
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mask_info_flow=True, |
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num_stages=3, |
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stage_loss_weights=[1, 0.5, 0.25], |
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bbox_roi_extractor=dict( |
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type='SingleRoIExtractor', |
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roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), |
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out_channels=256, |
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featmap_strides=[4, 8, 16, 32]), |
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bbox_head=[ |
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dict( |
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type='Shared2FCBBoxHead', |
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in_channels=256, |
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fc_out_channels=1024, |
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roi_feat_size=7, |
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num_classes=80, |
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bbox_coder=dict( |
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type='DeltaXYWHBBoxCoder', |
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target_means=[0., 0., 0., 0.], |
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target_stds=[0.1, 0.1, 0.2, 0.2]), |
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reg_class_agnostic=True, |
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loss_cls=dict( |
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type='CrossEntropyLoss', |
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use_sigmoid=False, |
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loss_weight=1.0), |
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loss_bbox=dict(type='SmoothL1Loss', beta=1.0, |
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loss_weight=1.0)), |
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dict( |
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type='Shared2FCBBoxHead', |
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in_channels=256, |
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fc_out_channels=1024, |
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roi_feat_size=7, |
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num_classes=80, |
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bbox_coder=dict( |
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type='DeltaXYWHBBoxCoder', |
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target_means=[0., 0., 0., 0.], |
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target_stds=[0.05, 0.05, 0.1, 0.1]), |
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reg_class_agnostic=True, |
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loss_cls=dict( |
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type='CrossEntropyLoss', |
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use_sigmoid=False, |
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loss_weight=1.0), |
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loss_bbox=dict(type='SmoothL1Loss', beta=1.0, |
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loss_weight=1.0)), |
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dict( |
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type='Shared2FCBBoxHead', |
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in_channels=256, |
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fc_out_channels=1024, |
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roi_feat_size=7, |
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num_classes=80, |
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bbox_coder=dict( |
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type='DeltaXYWHBBoxCoder', |
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target_means=[0., 0., 0., 0.], |
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target_stds=[0.033, 0.033, 0.067, 0.067]), |
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reg_class_agnostic=True, |
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loss_cls=dict( |
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type='CrossEntropyLoss', |
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use_sigmoid=False, |
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loss_weight=1.0), |
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loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) |
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], |
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mask_roi_extractor=dict( |
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type='SingleRoIExtractor', |
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roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), |
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out_channels=256, |
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featmap_strides=[4, 8, 16, 32]), |
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mask_head=[ |
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dict( |
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type='HTCMaskHead', |
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with_conv_res=False, |
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num_convs=4, |
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in_channels=256, |
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conv_out_channels=256, |
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num_classes=80, |
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loss_mask=dict( |
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type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), |
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dict( |
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type='HTCMaskHead', |
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num_convs=4, |
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in_channels=256, |
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conv_out_channels=256, |
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num_classes=80, |
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loss_mask=dict( |
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type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), |
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dict( |
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type='HTCMaskHead', |
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num_convs=4, |
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in_channels=256, |
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conv_out_channels=256, |
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num_classes=80, |
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loss_mask=dict( |
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type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)) |
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]), |
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|
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train_cfg=dict( |
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rpn=dict( |
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assigner=dict( |
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type='MaxIoUAssigner', |
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pos_iou_thr=0.7, |
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neg_iou_thr=0.3, |
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min_pos_iou=0.3, |
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ignore_iof_thr=-1), |
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sampler=dict( |
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type='RandomSampler', |
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num=256, |
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pos_fraction=0.5, |
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neg_pos_ub=-1, |
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add_gt_as_proposals=False), |
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allowed_border=0, |
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pos_weight=-1, |
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debug=False), |
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rpn_proposal=dict( |
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nms_pre=2000, |
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max_per_img=2000, |
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nms=dict(type='nms', iou_threshold=0.7), |
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min_bbox_size=0), |
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rcnn=[ |
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dict( |
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assigner=dict( |
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type='MaxIoUAssigner', |
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pos_iou_thr=0.5, |
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neg_iou_thr=0.5, |
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min_pos_iou=0.5, |
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ignore_iof_thr=-1), |
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sampler=dict( |
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type='RandomSampler', |
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num=512, |
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pos_fraction=0.25, |
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neg_pos_ub=-1, |
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add_gt_as_proposals=True), |
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mask_size=28, |
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pos_weight=-1, |
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debug=False), |
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dict( |
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assigner=dict( |
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type='MaxIoUAssigner', |
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pos_iou_thr=0.6, |
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neg_iou_thr=0.6, |
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min_pos_iou=0.6, |
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ignore_iof_thr=-1), |
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sampler=dict( |
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type='RandomSampler', |
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num=512, |
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pos_fraction=0.25, |
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neg_pos_ub=-1, |
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add_gt_as_proposals=True), |
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mask_size=28, |
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pos_weight=-1, |
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debug=False), |
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dict( |
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assigner=dict( |
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type='MaxIoUAssigner', |
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pos_iou_thr=0.7, |
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neg_iou_thr=0.7, |
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min_pos_iou=0.7, |
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ignore_iof_thr=-1), |
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sampler=dict( |
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type='RandomSampler', |
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num=512, |
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pos_fraction=0.25, |
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neg_pos_ub=-1, |
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add_gt_as_proposals=True), |
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mask_size=28, |
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pos_weight=-1, |
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debug=False) |
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]), |
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test_cfg=dict( |
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rpn=dict( |
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nms_pre=1000, |
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max_per_img=1000, |
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nms=dict(type='nms', iou_threshold=0.7), |
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min_bbox_size=0), |
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rcnn=dict( |
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score_thr=0.001, |
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nms=dict(type='nms', iou_threshold=0.5), |
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max_per_img=100, |
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mask_thr_binary=0.5))) |
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img_norm_cfg = dict( |
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) |
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict( |
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type='MultiScaleFlipAug', |
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img_scale=(1333, 800), |
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flip=False, |
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transforms=[ |
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dict(type='Resize', keep_ratio=True), |
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dict(type='RandomFlip', flip_ratio=0.5), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='ImageToTensor', keys=['img']), |
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dict(type='Collect', keys=['img']), |
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]) |
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] |
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data = dict( |
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val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) |
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|
